A New Generation of Modeling, Forecasting, and Predictive Analytics
Real science is a powerful, pervasive force in retailing today, particularly so for addressing the complex challenges of retail pricing. Gone are the days when a category manager could trust in intuition and experience alone.
Price elasticity is the single most powerful driver of demand modeling, and is the critical enabler of the forecasting and predicting attributes of new-era price optimization systems. Price elasticity applies advanced math, statistics, and analytics to measure shopper response - sensitivity - to changes in price.
Done right, the application of scientific principles to the creation of a true price optimization strategy can lead to a significant sales, margin, and profit lift for retailers.
The Right Science Based Approach
New-era retail price optimization regimes are derived from integrating advanced statistics, mathematics, physics, and econometrics with traditional retailer best practices and heuristics. The new-era pricing regime, however, is more than just a mix of formulas; it is an adaptive process that integrates advanced modeling, analytics, forecasting, and workflow processes designed to provide retailers with a simple-to-use, easy-to-understand, fully integrated price, promotion and markdown optimization system.
Key differentiators between price optimization solutions:
- Sophisticated yet usable science. Integrates and balances business rules, strategies and operational policies into the optimization model and delivers recommendations in an intuitive, user-friendly manner which transparently provides the user with details on the application and prioritization of the rules and science used to derive the price.
- Self-learning demand models to respond quickly and accurately to changing market conditions. Demand models need to be updated automatically, without costly service fees, to ensure they reflect current and emerging shopper sentiment.
- Speed-to-Insights. Retailers should expect pricing strategy insights, transparent price recommendations and what-if scenario simulation results to be delivered at the point of decision in minutes- not days.
- Customizable for retailer specific needs. Most systems have been built for general purposes and adapted for retail use. Retailers should understand the level of retail know-how and adaptability to retail environments that a provider offers.
- Highly scalable at multiple levels: At the item level through to category, for any size retailer with any number of stores, with granularity and insight into pricing at the item/location level, for retailers across a variety of industries.
- Impact of data quality on forecast confidence. The more volatile the data, the less confidence the retailer has in the predictions produced by the system - price recommendations should account for this lack of confidence.
“Revionics helps us to understand the collective impact of competitive activity, cost changes, and consumer demand, then recommends the best weekly price changes for our locations. Price optimization helps us to put our customers’ needs first."
Pat Pessotto, Vice President of Merchandising, Longo’s
Blending Art and Science
Done right, new-era price optimization systems take the guesswork out of pricing – at every stage of a product’s life. Advanced science embedded in these systems is capable of providing specific pricing answers – but only in conjunction with the “art” of how a retailer wants their store to be positioned in the marketplace.
Information is power, and a good blend of science and rules based price optimization empowers retailers with the analyses, recommendations and measurements that allow them to improve profits, price image, and customer satisfaction.
Revionics Life Cycle Pricing uses sales data to produce mathematical models of customer demand, then makes informed, high-confidence predictions of customer behavior based on these models, allowing the retailer to make the best decisions for pricing, promotions, and markdowns in light of the strategic goals for their business, their competitive positioning and response to competitive price changes and promotions, the impact of weather and seasonality on product life cycles in different regions, the variation in elasticity attributed to demographic and price sensitivity characteristics in different locations, as well as their own individual “rules” and best-practices.
Decisions based on a good understanding of shopper demand (through elasticity modeling) allow retailers to boost revenue and profit, reduce waste, and save valuable time and effort across the entire organization, from planning to in-store execution.
In addition to analyzing shopper demand signals, modeling price changes to recommend new ones, and forecasting customer behavior based on recommended price changes, our price optimization science allows retailers to actually simulate alternate strategies and scenarios in order to predict the impact of recommended price changes, promotions and markdowns on future sales, margin, and profit metrics - for not only the specific product or category but also for products impacted by affinities and cannibalization. This predictive ability is brought about by the application of scientific disciplines that include mechanisms for not only generating prices, but also monitoring the effectiveness of price changes, learning from those results, and then adapting the underlying mathematical models that drive the science. Indeed, our retail pricing software is self-learning.
Our software as a service model requires us to fulfill our obligation, and be a true partner to our clients, each and every day - and our near 100% retention rate is an attestation to our continuous levels of customer support and satisfaction!
Over 30,000 retail sites a day are receiving our strategic recommendations. Like them, you can make decisions with confidence knowing that they are rooted in a scientific understanding of actual consumer behavior and sensitivity while supporting the operational policies, rules and brand considerations that have traditionally guided retailers.
“We realized we needed the science of a system in order to understand the price sensitivity of the items we sell, which offers an elasticity of demand calculation. A system removes some of the emotion that is built into pricing decisions and allows us to develop pricing that is consistent with our view of the strategic role of each category."
Dean Solyntjes, Director of Pricing and Business Support, Holiday Stationstores